Parameter adjusting method of unmanned aerial vehicle attitude controller based on linear active disturbance rejection control

Abstract

The invention relates to the technical field of unmanned aerial vehicle flight control, in particular to a parameter adjusting method of an unmanned aerial vehicle attitude controller based on linear active disturbance rejection control, which comprises the following steps: S10, constructing an attitude controller based on an LADRC principle, and setting adjustable parameters of the attitude controller; s20, migrating the attitude controller to the unmanned aerial vehicle firmware, and setting an initial value of an adjustable parameter according to an unmanned aerial vehicle platform parameter; s30, enabling the unmanned aerial vehicle to fly in a preset motion capture area, and obtaining first attitude information in the unmanned aerial vehicle flight process through a motion capture system; s40, performing data fusion according to the first attitude information and second attitude information collected by the unmanned aerial vehicle to obtain fused attitude information; and analyzing the performance of the current attitude controller according to the fused attitude information and remote control signal, and optimizing the adjustable parameters of the current attitude controller according to the performance. The parameter adjustment process is effectively simplified, the parameter adjustment difficulty is reduced, the attitude measurement precision is improved, and engineering of the unmanned aerial vehicle is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle flight control, and in particular to a parameter adjustment method of an unmanned aerial vehicle attitude controller based on linear active disturbance rejection control. Background Art

[0002] Multi-rotor UAVs have been widely used in many fields due to their low cost, easy operation, good maneuverability, and zero risk to personnel, and have broad development prospects. During the flight of micro-quadcopter UAVs, the physical effects acting on the UAVs are complex, such as gravity, gyroscopic torque, rotor inertia moment, and aerodynamics, and are easily disturbed by the external environment. Therefore, it is difficult to establish an effective and reliable dynamic model. For micro-UAVs, the influence of rotor aerodynamics under low Reynolds number conditions must also be considered.

[0003] The existing UAV control method based on ADRC (active disturbance rejection control) contains a large number of nonlinear optimal functions, which makes theoretical analysis very difficult, parameter adjustment is also cumbersome, and inconvenient to use in practical engineering applications. In addition, ADRC has many parameters, has strict requirements on the appropriate parameter domain, and the parameters of ADRC affect each other, and the performance visualization is not high. It is not easy to adjust the parameters of the ADRC controller to make it usable in actual engineering flights.

[0004] In addition, the existing UAV attitude controller parameter adjustment is usually based on the UAV's own posture information collected during the flight to determine the flight status of the UAV. However, due to noise interference and drift, the attitude measurement accuracy of this posture information is poor. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a parameter adjustment method for a UAV attitude controller based on linear anti-disturbance control, which solves the technical problems of high difficulty in parameter adjustment of the UAV attitude controller and poor attitude measurement accuracy.

[0007] (II) Technical solution

[0008] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0009] In a first aspect, an embodiment of the present invention provides a parameter adjustment method for a UAV attitude controller based on linear active disturbance rejection control, comprising:

[0010] S10, constructing a posture controller based on the LADRC (linear active disturbance rejection control) principle, and setting adjustable parameters of the posture controller; the adjustable parameters of the posture controller include: outer loop gain, system coefficient, observer gain and controller gain;

[0011] S20, migrating the attitude controller to the drone firmware, and setting the initial value of the adjustable parameter according to the drone platform parameters;

[0012] S30, causing the drone to fly in a preset motion capture area, and obtaining first posture information of the drone during flight through a motion capture system;

[0013] S40, performing data fusion based on the first attitude information and the second attitude information collected by the drone itself to obtain fused attitude information; analyzing the performance of the current attitude controller based on the fused attitude information and the remote control signal during the flight of the drone, and optimizing the adjustable parameters of the current attitude controller based on the performance.

[0014] Optionally, in S10, the attitude controller includes: a roll control loop, a pitch control loop and a yaw control loop, each control loop includes:

[0015] A P control unit is used to determine the expected angular velocity of the UAV using a proportional control method based on an outer loop gain P according to an angular error between the expected angle and the real-time angle of the UAV; the expected angular velocity includes: an expected rolling angular velocity, an expected pitch angular velocity, and an expected yaw angular velocity;

[0016] The LADRC control unit is used to determine the control parameters of the UAV according to the expected angular velocity of the UAV, based on the system coefficient, observer gain and controller gain, using the LADRC control method to enable the UAV to adjust its own posture according to the control parameters; the control parameters include: lateral differential control amount, longitudinal differential control amount and heading differential control amount.

[0017] Optionally, the LADRC control unit comprises:

[0018] A linear extended state observer is used to establish a state space expression of the unmanned disturbance state according to the output value of the LADRC control unit on the control parameter and the real-time value of the control parameter of the UAV, the outer loop gain, the system coefficient and the observer gain;

[0019] A disturbance compensator is used to solve the control parameters based on the control law according to the state space expression.

[0020] Optionally, in the linear extended state observer, establishing a state space expression for an unperturbed state includes:

[0021] The state space expression is:

[0022]

[0023] Among them, β 1 =3ω 0 , β 2 =3ω o 2 , β 3 =ω o 3 ,ω 0 represents the observer gain, b 0 represents the system coefficient, u represents the output value of the LADRC control unit regarding the control parameter, y represents the real-time value of the control parameter of the drone, and z i ≈x i ,i=1,2,3;x 1 =y, x 3 =f, represents the first-order differential of y, and f represents the total disturbance.

[0024] Optionally, in the disturbance compensator, solving the control parameter based on the control law includes:

[0025] The control law is:

[0026]

[0027] in, k d =2ω c ,ω c represents the controller gain and r is the desired angular velocity.

[0028] Optionally, in S20, the UAV platform parameters include: at least one of the weight, size and model of the UAV.

[0029] Optionally, in S20, setting the initial value of the adjustable parameter according to the UAV platform parameter includes:

[0030] System coefficient b 0 The value range is: [100,5000];

[0031] Observer gain ω 0 The value range is: [1,100];

[0032] Controller gain ω cThe value range is: [0.05m, 0.2m], where m represents the weight of the drone.

[0033] Optionally, in S30, the acquiring the first posture information during the unmanned flight process by using a motion capture system includes:

[0034] Reflective marking points are unevenly arranged on the body of the drone, and multiple motion capture lenses of the motion capture system acquire first posture information of the drone through the reflective marking points from multiple angles.

[0035] Optionally, the S40 includes:

[0036] The ground station host communicates with the UAV in real time to obtain the second attitude information collected by the inertial measurement unit of the UAV, and the ground station host fuses the first attitude information and the second attitude information through a data fusion algorithm to obtain high-precision, low-noise fused attitude information;

[0037] The ground station host parses the fused attitude information and remote control information, and digitizes and visualizes the performance of the UAV and the current attitude controller, and uses a parameter optimization algorithm to optimize the adjustable parameters of the current attitude controller to obtain optimized adjustable parameters; and,

[0038] The ground station host communicates with the UAV in real time and sends the optimized adjustable parameters to the UAV so that the UAV can optimize the performance of the attitude controller in real time.

[0039] Optionally, in S40, the ground station host fuses the first posture information and the second posture information through a data fusion algorithm to obtain high-precision, low-noise fused posture information, including:

[0040] A1. Time-synchronize the first posture information and the second posture information so that the first posture information and the second posture information have the same time resolution;

[0041] A2. Correcting the first posture information using an extended Kalman filter according to the second posture information;

[0042] A3. Dynamically adjust the weight of the first posture information and the weight of the second posture information according to the signal quality of the first posture information;

[0043] A4. According to the weight of the first posture information and the weight of the second posture information, weight the first posture information and the second posture information to obtain fused posture information.

[0044] (III) Beneficial effects

[0045] The parameter adjustment method proposed in the present invention enriches the original PID control architecture for attitude control with LADRC control, and builds an optical motion capture system so that the motion capture system and the drone communicate with each other. Finally, the parameters are adjusted according to the attitude information during the unmanned flight process so that the controller meets the expected performance. The attitude controller proposed in this embodiment is based on the LADRC control principle, requires fewer parameters to be debugged, has a short design cycle, and is more suitable for engineering applications. Moreover, when analyzing the performance of the current attitude controller, the parameter adjustment method of the present invention uses the fused attitude information as the posture data of the drone, that is, the high dynamic responsiveness of the second attitude information is used to compensate for the low update rate of the first attitude information, and the high precision and drift-free characteristics of the first attitude information are used to correct the accumulated error of the second attitude information, thereby improving the attitude measurement accuracy of the drone, reducing the feedback noise in the control system, and then improving the stability of the subsequent parameter optimization algorithm, improving the optimality of the online performance optimization of the attitude controller, and improving the adaptability of the drone to complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flow chart of a parameter adjustment method of a UAV attitude controller based on linear active disturbance rejection control provided in an embodiment;

[0047] Figure 2 Schematic diagram of the structure of a LADRC control unit in the embodiment;

[0048] Figure 3 A schematic diagram of a flight control system architecture of a UAV in an embodiment;

[0049] Figure 4 Schematic diagram of device communication during the parameter adjustment process of the drone in the embodiment. DETAILED DESCRIPTION

[0050] In order to better explain the present invention, so as to facilitate understanding, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a clearer and more thorough understanding of the present invention, and to be able to fully convey the scope of the present invention to those skilled in the art.

[0051] Embodiment 1

[0052] like Figure 1 As shown, the embodiment of the present invention provides a parameter adjustment method of a UAV attitude controller based on linear active disturbance rejection control, including steps S10 to S40, which are specifically as follows:

[0053] S10, constructing an attitude controller based on the LADRC principle, and setting adjustable parameters of the attitude controller; the adjustable parameters of the attitude controller include: outer loop gain, system coefficient, observer gain and controller gain.

[0054] S20, migrating the attitude controller to the drone firmware, and setting the initial value of the adjustable parameter according to the drone platform parameters.

[0055] Specifically, the UAV platform parameters include: at least one of the weight, size and model of the UAV.

[0056] S30, flying the drone in a pre-set motion capture area, and obtaining first posture information of the drone during flight through a motion capture system (MoCap). Specifically, the first posture information includes three-axis angular velocity and three-axis acceleration of the drone captured by the motion capture system.

[0057] S40, performing data fusion based on the first attitude information and the second attitude information collected by the drone itself to obtain fused attitude information; analyzing the performance of the current attitude controller based on the fused attitude information and the remote control signal during the flight of the drone, and optimizing the adjustable parameters of the current attitude controller based on the performance.

[0058] The parameter adjustment method proposed in this embodiment enriches the original PID control architecture for attitude control with LADRC control, and builds an optical motion capture system so that the motion capture system and the drone can communicate with each other. Finally, the parameters are adjusted according to the attitude information during the unmanned flight process so that the controller meets the expected performance. The attitude controller proposed in this embodiment is based on the LADRC control principle, requires fewer parameters to be debugged, has a short design cycle, and is more suitable for engineering applications.

[0059] In a specific implementation of this embodiment, in S10, the attitude controller includes: a roll control loop, a pitch control loop and a yaw control loop, each control loop includes: a P control unit and a LADRC control unit, as follows:

[0060] The P control unit is used to determine the expected angular velocity of the UAV based on the angular error between the expected angle and the real-time angle of the UAV and based on the outer loop gain P using a proportional control method; the expected angular velocity includes: an expected roll angular velocity, an expected pitch angular velocity and an expected yaw angular velocity.

[0061] The LADRC control unit is used to determine the control parameters of the UAV according to the expected angular velocity of the UAV, based on the system coefficient, observer gain and controller gain, using the LADRC control method to enable the UAV to adjust its own posture according to the control parameters; the control parameters include: lateral differential control amount, longitudinal differential control amount and heading differential control amount.

[0062] The above-mentioned attitude controller provided in this embodiment is actually a two-layer control architecture in which the attitude inner loop control adopts LADRC control and the attitude outer loop control adopts P control. This architecture combines the strong robustness and interference suppression ability of LADRC with the simplicity, efficiency and versatility of P control, and can achieve fast and high-precision attitude control in complex environments.

[0063] This two-layer control architecture divides the attitude control task into two levels:

[0064] Inner loop (LADRC control): quickly responds to changes in the attitude angle of the drone and is responsible for dealing with high-frequency dynamic characteristics, external disturbances and system nonlinear problems.

[0065] Outer loop (P control): retains its simple and intuitive characteristics, generates the desired attitude angular velocity reference value, and solves the problem of slow dynamic adjustment.

[0066] Through this division of labor, the control objectives of the inner and outer loops are clearer, simplifying the design difficulty of each, which helps to improve the overall control performance of the system. The design of the controller is more modular, which is easy to debug and optimize.

[0067] The existing pure PID control has the problems of limited robustness, weak anti-interference ability, slow response speed and large overshoot. This embodiment introduces the LADRC controller in the inner loop of the attitude, which significantly improves the anti-interference ability and fault tolerance of the attitude controller, reduces the dependence on sensor noise and hardware defects, and better plays the strong robustness of the system, so that it can still maintain stable operation under the conditions of parameter uncertainty and large model error. In addition, the attitude controller provided in this embodiment has a significant improvement in dynamic response speed, and the overshoot and steady-state error are significantly reduced, thereby achieving a faster, more accurate and stable control effect. This improvement in dynamic performance significantly improves the controllability and mission execution quality of the UAV, especially in fast maneuvering tasks.

[0068] Specifically, Figure 2 As shown, the LADRC control unit includes: a linear extended state observer (LESO) and a disturbance compensator, which are as follows:

[0069] The linear extended state observer is used to establish a state space expression for the unmanned disturbance state according to the output value of the LADRC control unit on the control parameter and the real-time value of the control parameter of the UAV, according to the outer loop gain, the system coefficient and the observer gain.

[0070] The state space expression is:

[0071]

[0072] Among them, β 1 =3ω 0 , β 2 =3ω o 2 , β 3 =ω o 3 ,ω 0 represents the observer gain, b 0 represents the system coefficient, u represents the output value of the LADRC control unit regarding the control parameter, y represents the real-time value of the control parameter of the drone, and z i ≈x i ,i=1,2,3;x 1 =y, x 3 =f, represents the first-order differential of y, and f represents the total disturbance.

[0073] In formula (1), the observer gain ω 0 Sometimes also called the observer bandwidth, it is the only parameter to be adjusted in the linear extended state observer, ensuring that the linear extended state observer is BIBO stable.

[0074] The disturbance compensator is used to solve the control parameters based on the control law according to the state space expression.

[0075] The control law is:

[0076]

[0077] in, k d =2ω c ,ω c represents the controller gain and r is the desired angular velocity.

[0078] Based on the above formula (2), the disturbance compensator obtains the disturbance of the UAV flight control system from the linear extended state observer, and obtains active disturbance suppression by subtracting the observed disturbance.

[0079] Corresponding to the above-mentioned attitude controller, in S20, the initial value of the adjustable parameter is set according to the UAV platform parameter, including:

[0080] System coefficient b 0 The value range is: [100,5000].

[0081] Observer gain ω 0 The value range is: [1,100].

[0082] Controller gain ω c The value range is: [0.05m, 0.2m], where m represents the weight of the drone.

[0083] Based on the above attitude controller, the flight control system architecture of the UAV is as follows Figure 3 As shown, in order to reduce the influence of coupling between the three channels of the roll control loop, the pitch control loop and the yaw control loop, the attitude controller provided in this embodiment is divided into three independent loops, and a feedback mechanism is introduced to ensure that the attitude angle can follow the input control command. In the attitude controller, the input of the roll angle φ and the pitch angle θ can be either the output of the outer loop control loop φ, θ, or the direct command input φ_cmd, θ_cmd; the yaw angle ψ only obtains a direct control command. In the attitude controller, three pairs of independent P-LADRC controllers (P control unit + LADRC control unit) are used for fusion control of the three channels. The LADRC control unit is used from the angular velocity loop to the control parameters of the drone, and the P control unit is used from the attitude angle loop to the angular velocity loop. The three pairs of independent P-LADRC controllers are decoupled and controlled respectively for the three control quantities of the roll angle φ, the pitch angle θ and the yaw angle ψ, and the control parameters of the drone are output: the lateral differential control amount δx, the longitudinal differential control amount δy and the heading differential control amount δz.

[0084] Based on the above attitude controller, users only need to adjust four parameters during the parameter adjustment process: outer loop gain, system coefficient, observer gain and controller gain. This effectively simplifies the parameter adjustment process, reduces the difficulty of UAV parameter adjustment, and facilitates the engineering of UAVs.

[0085] Embodiment 2

[0086] Based on the attitude controller provided in the first embodiment, this embodiment provides a specific parameter adjustment method, which specifically includes:

[0087] S10, constructing the attitude controller provided in the first embodiment based on the LADRC principle, and setting the adjustable parameters of the attitude controller. The adjustable parameters of the attitude controller include: outer loop gain, system coefficient, observer gain and controller gain. The attitude controller can be implemented based on Ardupilot code.

[0088] S20, migrating the attitude controller to the drone firmware, and setting the initial value of the adjustable parameter according to the drone platform parameters.

[0089] Specifically, the drone is a quad-rotor drone, which is equipped with the following components: 2 pairs of 9450cw propellers (i.e., blade diameter 9.4 inches, pitch 5.0 inches); 4 SUNNYSKY brushless motors, A2212-13, KV: 980CW; 4 Hobbywing XRotor 20A ESCs, 3-4S Lipo batteries; Grignard ACE 3300mAh 25C, 14.8V-4S1P48.8Wh model aircraft battery; Futaba R3008SB 8-Ch (T-FHSS / S.Bus2) receiver; and Pixhawk flight control equipment. The drone weighs 1.16kg.

[0090] Flash the drone platform parameters and the above-mentioned attitude controller into the drone's flight control board, calibrate the sensor, set the flight mode, calibrate the ESC, and connect the Bluetooth module to achieve wireless two-way communication to adjust the parameters in real time. Set the initial values ​​of the adjustable parameters according to the weight and size of the drone. 0 The initial value of can be 100-5000, ω 0 The initial value of can be 1-100, ω c The initial value of is greatly affected by the model of the drone and can be taken as 1 / 20-1 / 5 of the weight of the whole drone. Some parameters are shown in Table 1.

[0091] Table 1 Initial values ​​of some adjustable parameters of the attitude controller

[0092]

[0093] S30, making the UAV fly in a preset motion capture area, and obtaining first posture information of the UAV during the flight process through a motion capture system.

[0094] Specifically, the motion capture area is enclosed by a truss and a protective net.

[0095] The drone body is unevenly provided with reflective marking points. The drone is regarded as a rigid body, and multiple motion capture lenses of the motion capture system acquire first posture information of the drone from multiple angles through the reflective marking points.

[0096] Specifically, during the flight of the drone, the motion capture lenses corresponding to the eight motion capture devices collect motion capture signals under a preset range of light intensity when the drone is flying, with a collection frequency of 60 Hz, and then establish the drone's position information based on the motion capture signals. Figure 4As shown, the motion capture IP of the motion capture system pushes the established drone's position and posture information to other computer hosts in the same IP network segment, that is, the host IP where the ground station is located, through broadcast data, and converts it into the drone's first position and posture information (MoCap data).

[0097] S40, performing data fusion according to the first attitude information and the second attitude information collected by the drone itself to obtain fused attitude information; analyzing the performance of the current attitude controller according to the fused attitude information and the remote control signal during the flight of the drone, and optimizing the adjustable parameters of the current attitude controller according to the performance, specifically as follows: Figure 4 shown.

[0098] More preferably, step S40 provided in this embodiment specifically includes sub-steps S401 to S403:

[0099] S401, the ground station host communicates with the drone in real time to obtain the second attitude information collected by the drone's inertial measurement unit (IMU), and the ground station host fuses the first attitude information and the second attitude information through a data fusion algorithm to obtain high-precision, low-noise fused attitude information. Specifically, the second attitude information includes the three-axis angular velocity and three-axis acceleration of the drone collected by the IMU.

[0100] The complementary information provided by IMU (Inertial Measurement Unit) and MoCap (Motion Capture) is integrated through the data fusion algorithm, and the advantages of both are used to make up for their respective shortcomings, so as to generate high-precision, low-noise attitude information, thereby providing important support for the parameter optimization algorithm in step S402, accelerating the convergence speed, improving the optimality, and supporting the adaptive parameter adjustment of the drone.

[0101] This embodiment effectively integrates the high dynamic response capability of IMU (second attitude information) and the high precision characteristics of MoCap (first attitude information) through a data fusion algorithm, which can significantly improve the attitude measurement accuracy of the UAV in a dynamic environment and reduce the impact of noise and drift on the measurement results. In other words, the high dynamic responsiveness of IMU (second attitude information) compensates for the low update rate of MoCap (first attitude information); the high precision and drift-free characteristics of MoCap (first attitude information) are used to correct the cumulative error of IMU (second attitude information). At the same time, accurate attitude information can further optimize the UAV's adaptive parameter adjustment algorithm and accelerate the convergence speed of parameters, thereby improving the control performance and environmental adaptability of the UAV.

[0102] Specifically, the core of fusing IMU data (second posture information) and MoCap data (first posture information) is to fuse them using their complementary characteristics. The data fusion algorithm in sub-step S401 specifically includes sub-steps A1 to A4:

[0103] A1. Time-synchronize the first posture information and the second posture information so that the first posture information and the second posture information have the same time resolution.

[0104] In fact, due to the high update rate of IMU data and the low update rate of MoCap, time synchronization of the first posture information and the second posture information in step A1 means using interpolation to increase the frame rate of MoCap data so that the MoCap data has the same time resolution as the IMU data.

[0105] A2. According to the second posture information, use an extended Kalman filter (EKF) to correct the first posture information.

[0106] That is, the extended Kalman filter (EKF) is used to predict the state information of the second attitude information of the drone according to the high-frequency dynamic information provided by the IMU, and based on the state information, the second attitude information is corrected in combination with the low-frequency and high-precision information of the MoCap data (first attitude information).

[0107] A3. Dynamically adjust the weight of the first posture information and the weight of the second posture information according to the signal quality of the first posture information.

[0108] Specifically, the weight of the first posture information and the weight of the second posture information may be dynamically adjusted according to the signal quality of the first posture information.

[0109] The conditions for judging that the signal quality of the first posture information is good include one or more of the following judgment conditions: the reflective mark point is not blocked, the signal is stable, and the noise variance is less than the first preset value. The conditions for judging that the signal quality of the first posture information is poor include one or more of the following judgment conditions: there is environmental interference, there is blockage of the reflective mark point, the noise variance is greater than the second preset value, and there is data loss.

[0110] When the signal quality of the first posture information is better, the weight of the first posture information is increased by a preset step size, and the weight of the second posture information is correspondingly decreased by a preset step size; similarly, when the signal quality of the first posture information is worse, the weight of the first posture information is decreased by a preset step size, and the weight of the second posture information is correspondingly increased by a preset step size.

[0111] Specifically, the sum of the weight of the first posture information and the weight of the second posture information is 1. Preferably, the value range of the preset step length may be [0.02, 0.5], and more preferably, the value of the preset step length is 0.1.

[0112] The above process of dynamically adjusting the weights can ensure the robustness of the fusion result according to the signal quality of the first posture information.

[0113] A4. According to the weight of the first posture information and the weight of the second posture information, weight the first posture information and the second posture information to obtain fused posture information.

[0114] Compared with pure IMU data (second attitude information), the fused attitude information compensates for the drift error of the IMU data (second attitude information), further improving the accuracy of attitude measurement. The above data fusion algorithm can quickly respond to attitude changes, so that the parameter optimization algorithm in S402 converges faster in a dynamic environment; moreover, the improvement of attitude measurement accuracy reduces the feedback noise in the control system, thereby improving the stability of the parameter optimization algorithm, thereby improving the optimality of the online performance optimization of the attitude controller, improving the adaptability of the drone to complex environments, and enabling the attitude controller to meet the expected performance.

[0115] S402, the ground station host parses the attitude information and remote control information, and digitizes and visualizes the performance of the UAV and the current attitude controller, and uses a parameter optimization algorithm to optimize the adjustable parameters of the current attitude controller to obtain optimized adjustable parameters.

[0116] In addition, by digitizing and visualizing the posture information and remote control signals, it is possible to accurately view the UAV's tracking effect on the remote control signal and various performances, facilitate real-time parameter adjustment, quickly obtain parameter adjustment effects, and improve parameter adjustment accuracy. After the performance of the current attitude controller is digitized and visualized, it can also interact with the user through the human-computer interaction interface to achieve artificial optimization of adjustable parameters, and integrate user experience into the parameter adjustment process.

[0117] S403: The ground station host communicates with the UAV in real time and sends the optimized adjustable parameters to the UAV, so that the UAV optimizes the performance of the attitude controller in real time.

[0118] In addition, the parameter adjustment method provided in this embodiment can also set waypoint information to optimize the trajectory tracking performance of the drone's designated channel; and / or, add external interference to optimize the anti-interference performance of the drone's designated channel attitude.

[0119] In this embodiment, the above-mentioned custom attitude controller is developed based on Ardupilot code and linear anti-disturbance theory, the drone is connected to a Bluetooth module, the initial parameters of the controller are set according to the size and weight of the drone, the posture information of the drone is acquired through the optical motion capture system, and the posture information from the motion capture system is pushed to the host IP where the ground station is located. The host IP adjusts the controller parameters of the three channels of the drone based on the posture information and the remote control signal. Based on the above steps S10 to S40, this embodiment realizes the simplification, rapidity, precision and engineering of the development and design of the drone attitude controller based on LADRC.

[0120] It should be noted that in the claims, any reference numerals placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by the same hardware. The use of the words first, second, third, etc., is for convenience of expression only and does not indicate any order. These words may be understood as part of the component name.

[0121] In addition, it should be noted that, in the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0122] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments after knowing the basic creative concept. Therefore, the claims should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0123] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include these modifications and variations.

Claims

1. A parameter adjustment method for an unmanned aerial vehicle attitude controller based on linear active disturbance rejection control, characterized in that: include: S10, constructing a posture controller based on the LADRC principle, and setting adjustable parameters of the posture controller; The adjustable parameters of the attitude controller include: outer loop gain, system coefficient, observer gain and controller gain; S20, migrating the attitude controller to the drone firmware, and setting the initial value of the adjustable parameter according to the drone platform parameters; S30, causing the drone to fly in a preset motion capture area, and obtaining first posture information of the drone during flight through a motion capture system; S40, performing data fusion based on the first attitude information and the second attitude information collected by the drone itself to obtain fused attitude information; analyzing the performance of the current attitude controller based on the fused attitude information and the remote control signal during the flight of the drone, and optimizing the adjustable parameters of the current attitude controller based on the performance.

2. The parameter adjustment method according to claim 1, characterized in that: In S10, the attitude controller includes: a roll control loop, a pitch control loop and a yaw control loop, each control loop includes: A P control unit is used to determine the expected angular velocity of the UAV using a proportional control method based on an outer loop gain P according to an angular error between the expected angle and the real-time angle of the UAV; the expected angular velocity includes: an expected rolling angular velocity, an expected pitch angular velocity, and an expected yaw angular velocity; The LADRC control unit is used to determine the control parameters of the UAV according to the expected angular velocity of the UAV, based on the system coefficient, observer gain and controller gain, using the LADRC control method to enable the UAV to adjust its own posture according to the control parameters; the control parameters include: lateral differential control amount, longitudinal differential control amount and heading differential control amount.

3. The parameter adjustment method according to claim 2, characterized in that: The LADRC control unit comprises: A linear extended state observer is used to establish a state space expression of the unmanned disturbance state according to the output value of the LADRC control unit on the control parameter and the real-time value of the control parameter of the UAV, the outer loop gain, the system coefficient and the observer gain; A disturbance compensator is used to solve the control parameters based on the control law according to the state space expression.

4. The parameter adjustment method according to claim 3, characterized in that: In the linear extended state observer, the state space expression for the unperturbed state is established, including: The state space expression is: Among them, β1=3ω0, β2=3ω o 2 , β3=ω o 3 , ω0 represents the observer gain, b0 represents the system coefficient, u represents the output value of the LADRC control unit regarding the control parameter, y represents the real-time value of the UAV regarding the control parameter, and z i ≈x i ,i=1,2,3;x1=y, x3=f, represents the first-order differential of y, and f represents the total disturbance.

5. The parameter adjustment method according to claim 4, characterized in that: In the disturbance compensator, the control parameter is solved based on the control law, including: The control law is: in, k d =2ω c ,ω c represents the controller gain and r is the desired angular velocity.

6. The parameter adjustment method according to claim 1, characterized in that: In S20, the drone platform parameters include: at least one of the weight, size and model of the drone.

7. The parameter adjustment method according to claim 6, characterized in that: In S20, setting the initial value of the adjustable parameter according to the UAV platform parameter includes: The value range of system coefficient b0 is: [100,5000]; The value range of observer gain ω0 is: [1,100]; Controller gain ω c The value range is: [0.05m, 0.2m], where m represents the weight of the drone.

8. The parameter adjustment method according to claim 1, characterized in that: In S30, the step of obtaining first posture information during the unmanned flight process by using a motion capture system includes: Reflective marking points are unevenly arranged on the body of the drone, and multiple motion capture lenses of the motion capture system acquire first posture information of the drone through the reflective marking points from multiple angles.

9. The parameter adjustment method according to claim 1, characterized in that: The S40 includes: The ground station host communicates with the UAV in real time to obtain the second attitude information collected by the inertial measurement unit of the UAV, and the ground station host fuses the first attitude information and the second attitude information through a data fusion algorithm to obtain high-precision, low-noise fused attitude information; The ground station host parses the first attitude information and remote control information, and digitizes and visualizes the performance of the UAV and the current attitude controller, and uses a parameter optimization algorithm to optimize the adjustable parameters of the current attitude controller to obtain optimized adjustable parameters; and The ground station host communicates with the UAV in real time and sends the optimized adjustable parameters to the UAV so that the UAV can optimize the performance of the attitude controller in real time.

10. The parameter adjustment method according to claim 9, characterized in that: In S40, the ground station host fuses the first posture information and the second posture information through a data fusion algorithm to obtain high-precision, low-noise fused posture information, including: A1. Time-synchronize the first posture information and the second posture information so that the first posture information and the second posture information have the same time resolution; A2. Correcting the first posture information using an extended Kalman filter according to the second posture information; A3. Dynamically adjust the weight of the first posture information and the weight of the second posture information according to the signal quality of the first posture information; A4. According to the weight of the first posture information and the weight of the second posture information, weight the first posture information and the second posture information to obtain fused posture information.

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